In this study, we assess how the anthropogenically induced increase ingreenhouse gas concentrations affects the climate of central and southernSouth America. We utilise two regional climate simulations for present day(PD) and pre-industrial (PI) times. These simulations are compared tohistorical reconstructions in order to investigate the driving processesresponsible for climatic changes between the different periods. The regionalclimate model is validated against observations for both re-analysis dataand GCM-driven regional simulations for the second half of the 20thcentury. Model biases are also taken into account for the interpretation ofthe model results. The added value of the regional simulation overglobal-scale modelling relates to a better representation of hydrologicalprocesses that are particularly evident in the proximity of the AndesMountains.Climatic differences between the simulated PD minus PI period agreequalitatively well with proxy-based temperature reconstructions, albeit theregional model overestimates the amplitude of the temperature increase. Forprecipitation the most important changes between the PD and PI simulationrelate to a dipole pattern along the Andes Mountains with increasedprecipitation over the southern parts and reduced precipitation over thecentral parts. Here only a few regions show robust similarity with studiesbased on empirical evidence. However, from a dynamical point-of-view,atmospheric circulation changes related to an increase in high-latitudezonal wind speed simulated by the regional climate model are consistent withnumerical modelling studies addressing changes in greenhouse gasconcentrations.Our results indicate that besides the direct effect of greenhouse gaschanges, large-scale changes in atmospheric circulation and sea surfacetemperatures also exert an influence on temperature and precipitationchanges in southern South America. These combined changes in turn affect therelationship between climate and atmospheric circulation between PD and PItimes and should be considered for the statistical reconstruction of climateindices calibrated within present-day climate data.
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